Giodiro
commented
1 year ago Hi @ahabedsoltan! Unfortunately the code which splits a dataset into blocks can produce weird behaviors, and sometimes this depends on factors such as the number of GPUs.
I just introduced an option to change the behavior of the heuristic for splitting the data into blocks: memory_slack. By default it is set to 0.9 which means that the split size is calculated considering 90% of available GPU RAM.
You try reducing it to e.g. 0.7 and the out of memory errors should go away.
Hi, I tried to run FALKON with 3 GPUs but I got the following error:
`Traceback (most recent call last): File "/home/"user"/.conda/envs/flk4/lib/python3.10/site-packages/falkon/utils/threading.py", line 15, in run self.ret = self._target(*self._args, **self._kwargs) File "/home/"user"//.conda/envs/flk4/lib/python3.10/site-packages/falkon/mmv_ops/fmmv.py", line 138, in mmv_run_starter return mmv_run_thread(X1, X2, v, out, kernel, blk_n, blk_m, mem_needed, dev, tid=proc_idx) File "/home/"user"//.conda/envs/flk4/lib/python3.10/site-packages/falkon/mmv_ops/fmmv.py", line 251, in mmv_run_thread flat_gpu = torch.empty(size=(mem_needed,), dtype=m1.dtype, device=dev) RuntimeError: CUDA out of memory. Tried to allocate 21.00 GiB (GPU 0; 31.75 GiB total capacity; 5.57 GiB already allocated; 20.88 GiB free; 9.56 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
The above exception was the direct cause of the following exception:
Traceback (most recent call last): File "/home/"user"/.conda/envs/flk4/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/home/"user"/.conda/envs/flk4/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/"user"/research/knotty/run/main.py", line 38, in
alpha, acc_valid_ep3,nystrom_samples,knots_x,acc_ep2_test= run(*args,wandb_run=wandb_run)
File "/home/"user"/research/knotty/run/run.py", line 225, in run
Falkon_loss, accu_falkon = falkon_run(dataset, kernel_fn, options, p=num_knots, epochs=20,
File "/home/"user"/research/knotty/run/run.py", line 34, in falkon_run
flk.fit(x_train, y_train)
File "/home/"user"/.conda/envs/flk4/lib/python3.10/site-packages/falkon/models/falkon.py", line 264, in fit
beta = optim.solve(
File "/home/"user"/.conda/envs/flk4/lib/python3.10/site-packages/falkon/optim/conjgrad.py", line 310, in solve
B = self.kernel.mmv(M, X, y_over_n, opt=self.params)
File "/home/"user"/.conda/envs/flk4/lib/python3.10/site-packages/falkon/kernels/kernel.py", line 266, in mmv
return mmv_impl(X1, X2, v, self, out, params)
File "/home/"user"/.conda/envs/flk4/lib/python3.10/site-packages/falkon/mmv_ops/fmmv.py", line 734, in fmmv
return KernelMmvFnFull.apply(kernel, opt, out, X1, X2, v, kernel.diff_params.values())
File "/home/"user"/.conda/envs/flk4/lib/python3.10/site-packages/falkon/mmv_ops/fmmv.py", line 695, in forward
KernelMmvFnFull.run_cpu_gpu(X1, X2, v, out, kernel, opt, False)
File "/home/"user"/.conda/envs/flk4/lib/python3.10/site-packages/falkon/mmv_ops/fmmv.py", line 641, in run_cpu_gpu
outputs = _start_wait_processes(mmv_run_starter, args)
File "/home/"user"/conda/envs/flk4/lib/python3.10/site-packages/falkon/mmv_ops/utils.py", line 59, in _start_wait_processes
outputs.append(p.join())
File "/home/"user"/.conda/envs/flk4/lib/python3.10/site-packages/falkon/utils/threading.py", line 22, in join
raise RuntimeError('Exception in thread %s' % (self.name)) from self.exc
RuntimeError: Exception in thread GPU-0
`
It works fine with 1,2 GPUs. I was wondering if using 3 or more GPUs can further make FALKON faster?
Thank you for your help.